{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mc_control.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM9uPO0SR85L7XQRRThdTJL",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/letianzj/QuantResearch/blob/master/ml/mc_control.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JlNkxcEuDTVn",
        "colab_type": "text"
      },
      "source": [
        "## Monte Carlo FrozenLake\n",
        "\n",
        "Use MC method to solve FrozenLake game.\n",
        "\n",
        "[OpenAI Gym FrozenLake](https://gym.openai.com/envs/FrozenLake-v0/) is defined on a 4x4 matrix. The agent wants to go from top left (S) to bottom right (G). Each grid can be either frozen surface (F) or hold (H). Four possible actions or moves are Left: 0, Down: 1, Right: 2, Up: 3. If the agent reaches goal (G), she receives reward of 1.\n",
        "\n",
        "MC control is on policy, Monte Carlo, mondel free method. It counts first visits to a state across episodes, and calculate avarage Q values in incremental format as\n",
        "\n",
        "$$\n",
        "\\begin{aligned}\n",
        "N(s, a) &\\leftarrow N(s, a) + 1  \\\\\n",
        "Q(s, a) &\\leftarrow Q(s,a) + \\frac{1}{N(s, a)}\\left( G - Q(s, a) \\right)  \\\\\n",
        "G &=r_t+\\gamma r_{t+1}+ \\gamma^2 r_{t+2} + \\cdots\n",
        "\\end{aligned}\n",
        "$$\n",
        "\n",
        "\n",
        "__Reference__\n",
        "* Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018.\n",
        "* [RL by David Silver](https://www.davidsilver.uk/teaching/)\n",
        "* [Lil'Log](https://lilianweng.github.io/lil-log/)\n",
        "* [Deep Learning with TensorFlow](https://github.com/dragen1860/Deep-Learning-with-TensorFlow-book)\n",
        "* [Reinforcement Learning](https://github.com/dennybritz/reinforcement-learning)\n",
        "* [marcinbogdanski](https://marcinbogdanski.github.io/reinforcement-learning.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0lOf7OSWHOvv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import gym"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtBE-yWBHZZh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "env = gym.make('FrozenLake-v0', is_slippery=True)   # may not head for the direction chosen"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QH8az9cJpmI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_episodes = 50000\n",
        "epsilon = 0.1               # epsilon greedy\n",
        "epsilon_final = 0.05\n",
        "epsilon_decay = (epsilon-epsilon_final)/n_episodes   # epsilon annealing\n",
        "gamma = 0.99                # discount factor\n",
        "\n",
        "# Q-Table, default to 0\n",
        "Q = np.zeros((env.observation_space.n, env.action_space.n))\n",
        "N = np.zeros([env.observation_space.n, env.action_space.n])\n",
        "# action space\n",
        "actions = range(env.action_space.n)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQotgJIHK4EW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "729da683-0d0f-4ed6-eba8-af98adc89c44"
      },
      "source": [
        "reward_history = []\n",
        "step_history = []\n",
        "\n",
        "for n_episode in range(n_episodes):\n",
        "    s = env.reset()          # reset to initial state\n",
        "    # env.render()\n",
        "    done = False\n",
        "    reward = 0.\n",
        "    step = 0\n",
        "    s_a_r_episode = []\n",
        "\n",
        "    # finish episode first\n",
        "    while not done:\n",
        "        # choose action according to epsilon greedy\n",
        "        a = 0\n",
        "        if np.random.rand() < epsilon:\n",
        "            a = env.action_space.sample()\n",
        "        else:   # randomly pick one in case of a tie\n",
        "            candidates = np.argwhere(Q[s, :] == np.amax(Q[s, :]))\n",
        "            candidates = candidates.reshape(-1,)\n",
        "            a = np.random.choice(candidates)\n",
        "\n",
        "        # use a to interact with environment\n",
        "        s_prime, r, done, info = env.step(a)\n",
        "        s = s_prime\n",
        "        s_a_r_episode.append([s, a, r])\n",
        "        step += 1\n",
        "        reward += r\n",
        "    \n",
        "    # Update all (state, action) pairs we've visited in this episode\n",
        "    sa_in_episode = set([(x[0], x[1]) for x in s_a_r_episode])\n",
        "    for state, action in sa_in_episode:\n",
        "        first_occurrence_idx = next(i for i,x in enumerate(s_a_r_episode) if x[0] == state and x[1] == action)\n",
        "        G = sum([x[2]*(gamma**i) for i,x in enumerate(s_a_r_episode[first_occurrence_idx:])])\n",
        "        N[state, action] += 1.0\n",
        "        alpha = 1.0 / N[state, action]\n",
        "        # update q values\n",
        "        Q[state, action] = Q[state, action] + alpha * (G - Q[state, action])\n",
        "\n",
        "    reward_history.append(reward)\n",
        "    step_history.append(step)\n",
        "\n",
        "    epsilon -= epsilon_decay\n",
        "\n",
        "    if (n_episode + 1) % 5000 == 0:\n",
        "        print(f'Episode {n_episode + 1}/{n_episodes}.')"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Episode 5000/50000.\n",
            "Episode 10000/50000.\n",
            "Episode 15000/50000.\n",
            "Episode 20000/50000.\n",
            "Episode 25000/50000.\n",
            "Episode 30000/50000.\n",
            "Episode 35000/50000.\n",
            "Episode 40000/50000.\n",
            "Episode 45000/50000.\n",
            "Episode 50000/50000.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJ5zWez2_dle",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "env.close()"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFX-ogw0zSQk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "6bb10a9a-efae-429e-f7f2-04944cc4f3df"
      },
      "source": [
        "df_stats = pd.DataFrame.from_dict({'rewards': reward_history, 'steps': step_history})\n",
        "fig, ax = plt.subplots(1, 2, figsize=(16,5))\n",
        "df_stats['rewards'].rolling(100).mean().plot(ax=ax[0])\n",
        "ax[0].set_title('Average rewards')\n",
        "df_stats['steps'].rolling(100).mean().plot(ax=ax[1])\n",
        "ax[1].set_title('Average steps')"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Average steps')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAmz6UWC_mex",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}